Noometry

Model comparison

GLM-4.5 vs Mixtral 8x22B

GLM-4.5 is the stronger model overall, scoring 42.0 to 27.1 on the Noometry Index.

Last verified . 18 shared benchmarks.

GLM-4.5 Z.ai (Zhipu)

42.0

Rank #122 Confirmed

Mixtral 8x22B Mistral AI

27.1

Rank #333 Confirmed

Summary

  • They share 18 benchmarks with published results for both. GLM-4.5 scores higher in 8 categories and Mixtral 8x22B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.5 leads 35.9 to 15.1.
  • The biggest single-benchmark swing is WeirdML: 40.6% for GLM-4.5 and 3.2% for Mixtral 8x22B.
  • GLM-4.5 is cheaper at $0.60 / $2.20 per million input/output tokens, against $2 / $6 for Mixtral 8x22B.
  • GLM-4.5 accepts more context: 131K tokens versus 64K.

Side by side

GLM-4.5Mixtral 8x22B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index42.027.1
Released2025-07-272024-04-17
WeightsOpenOpen
Context window131K64K
Max output98K64K
Input $ / M tokens$0.60$2
Output $ / M tokens$2.20$6
Results tracked2734

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Category by category

Coding GLM-4.5 leads

GLM-4.5: 41.4 (#125), Mixtral 8x22B: 24.2 (#329)

BenchmarkGLM-4.5Mixtral 8x22B
WeirdML40.6%3.2%
LMArena Coding14341166
SWE-bench Verified (bash only)54.2%—
BigCodeBench Instruct—40.6%
BigCodeBench Complete—50.2%
ALE-Bench344.82—
AlgoTune1.52—
HumanEval+—72%
MBPP+—64.3%

Agentic & Tool Use Not comparable

GLM-4.5: —, Mixtral 8x22B: 23.1 (#127)

BenchmarkGLM-4.5Mixtral 8x22B
Cybench—7.5%

Reasoning GLM-4.5 leads

GLM-4.5: 28.6 (#100), Mixtral 8x22B: 19.9 (#248)

BenchmarkGLM-4.5Mixtral 8x22B
LMArena Hard Prompts14291150
Kagi LLM Benchmark57.9%—
DTBench—55.1%
Epoch Capabilities Index—122.03
ForecastBench—56.3

Math GLM-4.5 leads

GLM-4.5: 39.0 (#116), Mixtral 8x22B: 22.9 (#275)

BenchmarkGLM-4.5Mixtral 8x22B
LMArena Math14271184
Omni-MATH—16.3%
MATH Level 5—24.2%

Knowledge GLM-4.5 leads

GLM-4.5: 35.9 (#179), Mixtral 8x22B: 15.1 (#293)

BenchmarkGLM-4.5Mixtral 8x22B
LMArena Expert14331113
GPQA Diamond—34.1%
Humanity's Last Exam8.3%—
MMLU-Pro—46%
Confabulations11.3%—
GPQA (HELM)—33.4%
MMLU—77.8%

Multilingual GLM-4.5 leads

GLM-4.5: 52.8 (#77), Mixtral 8x22B: 32.8 (#255)

BenchmarkGLM-4.5Mixtral 8x22B
LMArena Non-English14171128
LMArena Chinese14651116
LMArena French14181166
LMArena German14071141
LMArena Japanese14151037
LMArena Korean13801057
LMArena Russian14141158
LMArena Spanish14541151

Instruction Following GLM-4.5 leads

GLM-4.5: 74.1 (#104), Mixtral 8x22B: 57.7 (#266)

BenchmarkGLM-4.5Mixtral 8x22B
LMArena Instruction Following14041147
IFEval—72.4%

Long Context GLM-4.5 leads

GLM-4.5: 38.2 (#201), Mixtral 8x22B: 34.7 (#247)

BenchmarkGLM-4.5Mixtral 8x22B
LMArena Longer Query14121144
Fiction.LiveBench58.3%—

Writing & Preference GLM-4.5 leads

GLM-4.5: 57.5 (#127), Mixtral 8x22B: 36.9 (#262)

BenchmarkGLM-4.5Mixtral 8x22B
LMArena Text14301162
LMArena Creative Writing13951141
LMArena Multi-Turn14151130
Short-Story Creative Writing73.4%—
EQ-Bench Creative Writing1343—
WildBench—71.1%

Frequently asked questions

Is GLM-4.5 better than Mixtral 8x22B?

GLM-4.5 is the stronger model overall, scoring 42.0 to 27.1 on the Noometry Index.

Which is cheaper, GLM-4.5 or Mixtral 8x22B?

GLM-4.5 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Mixtral 8x22B lists at $2 and $6.

Is GLM-4.5 or Mixtral 8x22B better for coding?

GLM-4.5 scores higher on coding benchmarks: 41.4 versus 24.2 in the Noometry coding category.

Which has the bigger context window?

GLM-4.5 does, with 131K tokens against 64K.

How many benchmarks do GLM-4.5 and Mixtral 8x22B share?

18 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Mixtral 8x22B has 34.

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